MathWorks uses agentic AI to accelerate Simulink model adoption

MathWorks is integrating agentic AI into its ecosystem to help engineering teams migrate from rigid legacy workflows to new Simulink capabilities. The initiative aims to eliminate the friction typically associated with updating established engineering processes in complex systems.

Led by Sr. Principal Technologist Jason Ghidella and Consulting Advanced Support Engineer Guy Rouleau, the effort focuses on using AI agents to update functionality within Simulink. By automating the bridge between old methods and new features, MathWorks intends to reduce the manual effort and retraining usually required when engineering organizations adopt more efficient software capabilities. To ensure reliability, the company is emphasizing "grounded AI," an approach that integrates agents directly with simulation and verification tools to ensure that all outputs remain deterministic and traceable.

The Legacy Workflow Hurdle

Engineering organizations frequently rely on deeply entrenched, rigid workflows that make the adoption of new software tools a slow and difficult process. While Simulink is a standard for model-based design and simulation, the transition to newer, more efficient features often requires significant manual intervention. This "adoption tax" frequently discourages teams from upgrading their processes, leaving them tethered to outdated methods despite the availability of superior tools.

Ensuring Engineering Rigor

Unlike general-purpose AI, which can produce hallucinations or untraceable results, the MathWorks approach prioritizes the connection between AI output and empirical evidence. The necessity for this rigor is central to the project's design, as engineering systems require absolute precision for safety and performance.

"What often breaks is the connection between the AI-generated output and the engineering evidence needed to evaluate that output," Jason Ghidella noted, highlighting the critical need for grounded AI that can be verified through simulation.

Industry Implications

If agentic AI can successfully automate the migration of complex engineering models, the impact could be felt across the embedded systems market. Reducing the time and cost of process overhauls would allow firms to accelerate their time-to-market and leverage advanced simulation tools without the traditional productivity dip associated with software transitions. This shift could move the industry toward a more agile model of continuous tool improvement rather than infrequent, painful migrations.

Future Outlook

As MathWorks continues to refine these agentic workflows, the industry will be watching to see how effectively these tools handle the most complex, highly regulated engineering environments. The primary challenge remains the seamless integration of AI-driven updates with the strict verification and validation standards required for mission-critical hardware and software.